Journal of Economics, Finance and Accounting Studies
Founded in 2019, the Journal of Economics, Finance and Accounting Studies (JEFAS) is a double-blind peer-reviewed, open-access monthly journal published by Al-Kindi Center for Research and Development. The journal is led by expert international editorial board members who take an objective approach to peer review, ensuring each research paper is reviewed and evaluated on its own scholarly merits and research integrity. Publishing your work in the Journal of Economics, Finance and Accounting Studies means you will receive prompt and reliable publishing and a global platform for your research to reach its full potential. The journal is published in online version only.
Al-Kindi Center for Research and Development
Socio-Economic Impacts and Fiscal Vulnerabilities Following the February 6, 2023 Kahramanmaraş Earthquakes in Türkiye
The two major earthquakes (Mw 7.7 and Mw 7.6) that occurred on 6 February 2023, centered in Kahramanmaraş, produced profound socio-economic impacts in southeastern Türkiye. The disaster directly affected eleven provinces, caused widespread destruction, and resulted in more than 50,000 fatalities, while also leading to significant losses in Syria. Approximately 14 million people were directly affected by the physical destruction, and long-term socio-economic consequences are expected to persist. This study evaluates the direct and indirect economic losses caused by the earthquakes within a comprehensive framework, focusing on their impact on production, foreign trade, labor markets, and public finance. In this context, the Adaptive Regional Input–Output (ARIO) model, which enables time-dependent analysis of post-disaster economic impacts, is employed. The findings indicate that the earthquakes weakened regional production capacity, increased employment losses, and significantly expanded public expenditures, thereby deepening fiscal vulnerabilities. Population mobility has also led to structural changes in labor markets and national economic balances. Although reconstruction activities may support short-term recovery, sustainable growth requires integrated policy strategies that strengthen fiscal resilience, restore employment, and enhance long-term regional economic resilience and stability.
Socio-economic impacts, fiscal resilience, ARIO model, economic vulnerability
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AI-Driven Predictive Analytics Framework for Anti-Money Laundering Risk Management and Financial Infrastructure Protection in U.S. Banking Systems
U.S. banks run anti money laundering (AML) programs that must detect suspicious activity at scale, document decisions, and file timely Suspicious Activity Reports (SARs) while protecting SAR confidentiality (31 C.F.R. § 1020.320, 2023; FFIEC, 2021). Illicit finance increasingly exploits payment rails and multi account networks, raising compliance exposure and operational risk (FinCEN, 2021). This manuscript proposes a compliance aware, AI driven predictive analytics framework for U.S. banking. The framework integrates three layers: supervised alert ranking and suppression using calibrated gradient boosted trees (Chen & Guestrin, 2016); unsupervised anomaly detection for emerging typologies under label lag (Liu et al., 2008); and graph learning to capture relationship centered laundering structures such as mule rings and layering (Kipf & Welling, 2017; Hamilton et al., 2017). We specify a data and feature pipeline for deposits, wires, ACH, cards, and instant payments, including preprocessing, entity resolution, feature engineering, and class imbalance controls using cost sensitive objectives and precision recall analysis (Elkan, 2001; Saito & Rehmsmeier, 2015). Explainability is embedded as an evidence packet for investigators and validators using SHAP, LIME, counterfactuals, and graph explanations (Lundberg & Lee, 2017; Ribeiro et al., 2016; Wachter et al., 2018; Ying et al., 2019). Privacy preserving options, including federated learning, secure aggregation, and differential privacy, enable cross portfolio learning without centralizing sensitive customer data (Dwork et al., 2006; McMahan et al., 2017). Deployment guidance addresses real time scoring, latency targets, drift monitoring, and integration with case management and BSA e filing.
i-Money Laundering, Machine Learning, Graph Neural Networks, Financial Crime, National Security, Explainable AI, Privacy-Enhancing Technologies
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Design and Implementation of an AI-Augmented Autonomous Financial Operations Framework for Cloud-Native ERP Systems Using SAP BTP and RAP
Enterprise financial systems are undergoing rapid transformation due to increasing regulatory complexity, high-volume digital transactions, and the shift toward cloud-native architectures. Traditional ERP financial modules rely heavily on rule-based validations and manual reconciliation processes, limiting their ability to detect anomalies, prevent revenue leakage, and adapt dynamically to evolving compliance requirements. Despite the robustness of platforms such as SAP S/4HANA, financial operations in large enterprises continue to depend on static validations, post-period reconciliation, and manually supervised exception handling. These constraints increase fraud exposure, revenue leakage risk, and financial close cycle duration. This research introduces the Autonomous Financial Operations Framework (AFOF), a cloud-native, AI-augmented architecture that embeds intelligent automation directly within ERP transactional workflows. The framework integrates: The extensibility and microservices capabilities of SAP Business Technology Platform Behavior-driven service modeling via SAP ABAP RESTful Application Programming Model (RAP) Embedded machine learning services for anomaly detection and predictive analytics The proposed framework leverages the extensibility of SAP S/4HANA, the cloud capabilities of SAP Business Technology Platform, and the service-oriented programming paradigm of SAP ABAP RESTful Application Programming Model (RAP) to create a self-optimizing financial operations layer. The architecture introduces five core components: An Intelligent Posting Validation Engine using behavior-driven RAP logic. An AI-based Anomaly Detection Module for financial irregularities. Automated reconciliation services for high-volume subledger environments. Predictive revenue leakage analytics tailored for subscription-based monetization systems. A secure event-driven extension layer supporting scalable enterprise integration. A controlled enterprise-scale simulation was conducted using synthetic financial datasets modeling high-volume subscription billing environments (10–50 million monthly transactions). Comparative benchmarking against traditional rule-based ERP controls demonstrated: 43% reduction in financial close cycle duration 37% improvement in anomaly detection precision 52% reduction in manual reconciliation effort 28% decrease in revenue leakage exposure 31% faster exception resolution turnaround time Latency measurements confirmed that embedded AI validation introduced less than 8 ms average transactional overhead, preserving ERP performance integrity. Unlike conventional ERP enhancements that operate as external monitoring tools, the AFOF embeds machine learning–assisted controls directly within transactional workflows. This approach enables real-time compliance validation, adaptive financial risk scoring, and automated exception resolution, significantly reducing operational overhead and financial exposure. Performance modeling demonstrates measurable improvements in financial close cycle time, anomaly detection accuracy, and reconciliation efficiency when compared to legacy rule-based systems. This research contributes a scalable and reusable architectural model for AI-enabled financial automation in regulated industries, including telecommunications, digital commerce, healthcare billing and large-scale enterprise services. By integrating intelligent automation within mission-critical ERP systems, this research advances enterprise cybersecurity resilience, financial governance, and digital economic infrastructure modernization.
Design; Implementation; AI-Augmented Autonomous Financial Operations Framework; Cloud-Native ERP Systems; SAP BTP; RAP
Generative AI and U.S. Financial Reporting Integrity: Detecting Narrative Manipulation, Risk Disclosure Gaming, and Fraud Signals in 10-K Filings
U.S. capital markets rely on high-integrity corporate disclosure, yet the narrative portions of annual reports—particularly Management’s Discussion and Analysis (MD&A) and Risk Factors—remain vulnerable to strategic language management and, increasingly, generative-AI-assisted drafting. Unlike traditional misstatement detection that focuses on accounting ratios, narrative manipulation can distort investor beliefs through selective emphasis, obfuscation, boilerplate recycling, and inflated forward-looking language while remaining difficult to audit at scale. This paper develops a conceptual–methodological framework to measure disclosure integrity and detect narrative manipulation in U.S. 10-K filings. We construct a transparent EDGAR pipeline that parses MD&A and Risk Factors and extracts auditable features capturing (i) financial-context tone and uncertainty, (ii) readability and complexity, (iii) risk-factor novelty versus boilerplate drift (year-to-year similarity), (iv) forward-looking intensity, and (v) “tone–fundamentals gaps” that flag potential inconsistency between narrative claims and contemporaneous performance signals. We benchmark interpretable statistical models (logit/elastic net) against flexible machine-learning specifications while preserving explainability, and we evaluate performance using governance-relevant outcomes including enforcement-linked misstatement events and restatement proxies, complemented by market-based reactions around filing dates. We further test whether narrative-risk signals strengthen in the period associated with broad diffusion of generative AI writing tools. The proposed framework contributes to accounting, finance, and regulatory technology by converting narrative disclosure risk into measurable, monitorable indicators. Findings are positioned to inform SEC disclosure oversight, audit planning, and issuer governance by enabling scalable detection of narrative manipulation and risk-disclosure gaming that may undermine investor protection and U.S. market integrity.
10-K; MD&A; risk factors; disclosure integrity; textual analysis; narrative manipulation; generative AI; SEC enforcement; audit analytics; market integrity
FinTech for Climate Resilience: Measuring Insurance Gaps, Mortgage Stress, and Household Credit Risk in the United States
Climate-related hazards are increasingly transmitted into household balance sheets through the availability, affordability, and design of homeowners insurance. When premiums rise sharply, deductibles widen, or policies are non-renewed, households face larger out-of-pocket repair burdens and liquidity shocks that can elevate mortgage delinquency and broader consumer credit stress. These dynamics are not only a household welfare concern but also a national-interest issue for U.S. housing finance resilience: persistent insurance gaps can weaken loan performance, intensify regional housing volatility, and strain servicing and loss-mitigation capacity. This paper proposes a FinTech-oriented measurement and governance framework for climate resilience that links (i) county-level hazard exposure, (ii) insurance market frictions and coverage gaps, and (iii) mortgage and household credit outcomes in USA. Conceptually, the framework treats insurance gaps as a core mechanism of climate-to-credit transmission while positioning digital finance tools—InsurTech risk signals, embedded insurance/repair financing, and data-driven hardship identification—as levers to reduce amplification and improve consumer protection. Methodologically, we outline a public-data empirical strategy combining dynamic event studies around major hazard years with difference-in-differences designs exploiting heterogeneous insurance-supply tightening (e.g., non-renewal intensity and premium acceleration). We specify a replicable construct set and proxy map based on U.S. public sources (hazard indices, insurance market indicators, HMDA origination structure, house price dynamics, and consumer stress/complaint measures) and propose robustness checks addressing pre-trends, regional confounders, and distributional heterogeneity. To make the manuscript submission-ready, we include clearly labeled demonstration results generated on a simulated county-year panel calibrated to plausible ranges; these are placeholders that can be replaced with empirical estimates once the merged dataset is assembled. Policy implications focus on governance baselines for InsurTech-driven risk signals, transparency standards for insurance frictions, targeted consumer protections, and resilience metrics for housing finance intermediaries. The paper contributes a hybrid conceptual–methodological template enabling regulators, lenders, insurers, and FinTech platforms to measure and mitigate climate-driven insurance gaps before they translate into mortgage distress and broader household credit fragility.
climate resilience; InsurTech; homeowners insurance; non-renewals; mortgage delinquency; household credit risk; consumer protection; financial stability; governance; U.S. housing finance
A study of Implicit disagreement in MPC meetings for selected Eastern European Economies using the Taylor Rule
This paper in an empirical attempt to study implicit disagreement among MPC members of central banks contained in the minutes of the MPC meetings for three Eastern European economies, viz., Poland, Czech Republic and Hungary. NLP sentiment analysis techniques, specifically a BERT model trained and fine-tuned on the corpus of the text of the minutes of the MPC meetings of the three countries, are applied to extract sentence level sentiment scores in terms of hawkish, dovish or neutral sentiment which are then used to calculate meeting level Disagreement Index(DI) variable by taking the Standard Deviation of the sentence level sentiment scores. We augment the standard Taylor rule formula for setting monetary policy rates with this DI variable and see that it impacts the interest rate negatively, suggesting that higher implicit disagreement might restrain the MPC members from being too hawkish which might be influenced by higher uncertainty about economic conditions, especially fears of a looming recession. So, we find that disagreement has a significant impact, even implicit disagreement, on the policy rate decisions of the MPC members. This offers insights into why central banks might hesitate to raise rates immediately in face of higher inflation or output gap pressures.
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A Deterministic Trajectory-Level Evaluation Framework for Learning-Based Agentic Systems
In the recent past, learning-based agentic systems are being increasingly used to tackle complex decision environments where reliability, transparency, and compliance with governance are key. However, the dominant evaluation strategies are mostly outcome-centric, with a focus on aggregate performance measures like accuracy, rewards, or task completion rates. These evaluation strategies provide little information on the internal decision-making processes that lead to the observable outcomes, especially for reasoning-based agentic systems with multiple steps. This paper proposes a Deterministic Trajectory Level Evaluation Framework (DTLEF) for learning-based agentic systems. The proposed framework transforms the evaluation paradigm from traditional outcome-oriented metrics to an evaluation process that focuses on the trajectories of states and actions within controlled execution conditions. The DTLEF integrates standardized states, comprehensive action trace logging, deterministic replay validation, and behavior verification against governance constraints. The evaluation process, in which the agentic system runs in controlled inference mode and the trajectory traces are compared, enables the identification of policy instabilities, reasoning drift, and non-deterministic behavior. Moreover, the evaluation process also ensures that the trajectories are consistent with the predefined constraints. Unlike traditional performance metrics, the proposed framework does not depend upon empirical data or domain-specific metrics. Instead, it provides a methodology for evaluating agentic systems, including autonomous decision pipelines, tool-enhanced language agents, and cyber-physical control systems, at an architecture level. The proposed framework increases transparency, reproducibility, and compliance without modifying training procedures. This research provides a scalable and domain-independent evaluation methodology for validating learning-based autonomous agents in environments where behavioral reliability is as important as functional performance by formally defining trajectory-level determinism as a primary evaluation criterion.
Agentic Systems, Trajectory-Level Evaluation, Deterministic Replay, Learning-Based Autonomous Agents, AI Governance and Reliability
Impact of ERP-Enabled Digital Transformation on Cost Accounting Practices in Saudi Firms
Enterprise Resource Planning (ERP) systems are major instruments of organizational digital transformation as they integrate operational and financial processes and uplift the overall performance. This research focuses on the effect of ERP implementation on cost accounting activities in Saudi firms, the mediating roles of process integra-tion and automation being the primary emphasis. Information was obtained from 60 companies with a total of 290 employees from the manufacturing, construction, retail, energy, and service sectors. Reliability and validity were established through Cronbachs alpha, composite reliability, and average variance extracted. For data analysis, multiple regression, independent t, tests, and Partial Least Squares Structural Equation Modeling were utilized. Findings suggest that the adoption of ERP leads to a significant improve-ment in cost accuracy, cost control effectiveness, variance analysis quality, and managerial decision, making. In addition, process integration and automation partially mediate these relationships, thus advocating the strategic ERP adoption aligned with Saudi Vision 2030 goals.
Cost Accounting, ERP, Digital Transformation, Integration, cost accuracy, cost control effectiveness, variance analysis quality, managerial decision making
Machine Learning Models for Detecting Hidden Collusion Networks in U.S. Corporate Finance
Hidden collusion networks in U.S. corporate finance present a significant challenge to market integrity and regulatory oversight. Such networks are difficult to detect due to the indirect and distributed nature of collusive behavior, which is often embedded within legitimate financial transactions and governance relationships. This study aims to develop and evaluate machine learning models capable of detecting latent collusion structures by combining firm-level financial indicators with relational and governance-based network information. The goal is to uncover clusters of firms that exhibit coordinated behavior while providing interpretable insights for regulatory decision-making. A multi-stage detection framework was implemented, incorporating classical machine learning classifiers, tree-based ensembles, graph neural networks (GNNs), and hybrid models combining feature-driven and structure-driven learning. Corporate entities were represented as nodes within financial and governance networks, with edges encoding ownership ties, shared executives, transactional dependencies, and temporal co-movements. Models were evaluated using precision, recall, F1-score, AUC-ROC, AUC-PR, and network consistency metrics, while robustness analyses examined performance under class imbalance and sparse labeling conditions. Graph-based models outperformed traditional baselines, achieving F1-scores up to 0.91 and AUC-ROC values up to 0.94. Hybrid ensembles that combined tree-based and graph-based predictions achieved the highest overall performance (F1-score = 0.93, AUC-ROC = 0.95, AUC-PR = 0.91). The models successfully identified densely connected collusive clusters and key hub firms, highlighting the importance of relational and temporal features over isolated firm-level indicators. Ablation studies confirmed that financial metrics alone were insufficient to detect coordinated behavior without network structure. The study demonstrates that machine learning, particularly when integrated with graph-based relational representations, provides an effective and scalable approach for detecting hidden collusion networks in corporate finance. The proposed framework offers practical value for regulators, enabling probabilistic risk assessment, early-warning detection, and data-driven surveillance of potentially collusive activity.
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Real-Time Payments and Real-Time Fraud: A U.S. FinTech Risk Framework for RTP Rails and Consumer Protection
Real-time payment (RTP) rails convert retail payments from a batch, revocable process into an always-on, irrevocable, data-rich transfer of value. In the United States, this transition accelerated during 2012–2023 as mobile wallets, account-to-account (A2A) transfers, and instant credit transfers expanded, culminating in nationwide rail competition between private-sector networks and the Federal Reserve’s FedNow Service. The same design features that make RTP economically attractive—immediacy, finality, and API-based integration—also magnify fraud externalities and operational-resilience demands. Authorized push payment (APP) scams exploit consumer initiation and social engineering, while mule accounts, synthetic identities, and rapid cash-out compress detection windows from days to seconds. Regulatory liability regimes (e.g., U.S. Regulation E) were built around unauthorized transfers and do not fully internalize APP harms, creating incentives that may underinvest in preventive controls. This paper develops a pragmatic–institutional risk framework for U.S. RTP ecosystems that integrates (i) fraud typologies and externalities, (ii) operational-resilience requirements for 24/7 settlement, and (iii) consumer-protection and governance mechanisms spanning federal and state authorities. Methodologically, we specify a replicable measurement architecture using a simulated institution–region–year panel (2012–2023) that combines RTP intensity, fraud loss proxies, incident-response performance, and governance strength indicators. We outline identification strategies suitable for U.S. data constraints—fixed-effects panels, staggered adoption difference-in-differences, and instrumented uptake using broadband penetration and legacy payment frictions. Illustrative estimates show that RTP penetration is associated with a statistically meaningful rise in reported scam losses and complaint intensity in early adoption phases, while institutions with stronger control stacks—payee confirmation, transaction risk scoring, mule-network interdiction, and real-time case management—attenuate fraud growth and reduce tail operational incidents. We translate these findings into a U.S.-specific governance blueprint that aligns rail operators, depository institutions, nonbank PSPs, and regulators around a shared ‘prevent–detect–respond’ control taxonomy, standardized reporting metrics, and consumer redress principles. The framework supports policy goals of safer faster payments without suppressing innovation, and is designed to be extendable to cross-border instant payments dialogues. Research highlights. This paper develops a US-focused risk framework for real-time payments (RTP) rails and consumer protection across 2012–2023, bridging payment-systems design, fraud economics, and institutional governance. It clarifies why fast settlement increases the value of speed for legitimate users and for offenders, and shows how that shared incentive creates externalities that a single provider cannot fully price or internalize. Using a simulated institution–region–year panel, the study provides a replicable method for measuring (i) fraud externalities (losses shifted across consumers, banks, merchants and platforms), (ii) control effectiveness (prevention, detection, and response), and (iii) operational resilience (availability and recovery under stress). The results emphasize that the same features that make RTP socially valuable—immediacy, irrevocability, 24/7 availability—also intensify the downside of weak authentication, weak dispute design, and fragmented oversight. Policy-wise, the paper offers a coordinated control stack aligned to US agencies and industry bodies and proposes practical, auditable metrics (e.g., APP loss rate per 10k transactions, false-positive friction rate, time-to-recall, time-to-freeze, and recovery SLA compliance). The governance contribution is a ‘rails-and-responsibilities’ map that connects economic function to supervisory responsibility, supporting activity-based regulation and reducing arbitrage across state and federal regimes.
Real-time payments; FedNow; RTP network; authorized push payment scams; fraud externalities; operational resilience; consumer protection; U.S. financial regulation; payment governance
Algorithmic Accountability in U.S. Consumer FinTech: Governance Mechanisms for Credit Risk, Fair Lending, and Financial Stability
Financial technology (FinTech) has become a core component of the United States financial system, reshaping payment infrastructure, consumer credit markets, and data-driven financial intermediation. While FinTech innovation has improved efficiency and expanded financial access, its rapid scaling has also introduced new sources of systemic risk, regulatory fragmentation, and consumer vulnerability—particularly as artificial intelligence increasingly governs financial decision-making. This study examines FinTech innovation through an integrated governance lens, focusing on how AI-enabled intermediation interacts with financial stability and public-interest outcomes in the United States. Using a multi-source, institutionally grounded panel dataset spanning 2012–2022, the analysis conceptualizes FinTech as part of national financial infrastructure rather than as a peripheral technological disruption. The empirical strategy combines a composite measure of FinTech intensity with indicators of explainable AI adoption and institutional governance strength to assess their joint effects on financial stability risk and public-interest performance. Fixed-effects panel models, interaction specifications, and robustness checks are employed to isolate institutional and temporal dynamics. The results reveal three central findings. First, greater FinTech intensity enhances intermediation efficiency and financial inclusion but is associated with elevated short-term financial stability risks when deployed without adequate oversight. Second, the adoption of explainable AI significantly moderates these risks by reducing algorithmic opacity and improving auditability and supervisory visibility. Third, governance capacity—reflected in regulatory coordination, disclosure mandates, and supervisory engagement—emerges as a decisive factor in aligning technological innovation with systemic resilience and consumer trust. These findings suggest that the principal challenge facing the U.S. financial system is not technological innovation itself, but the institutional lag between innovation and governance. By demonstrating that explainable AI functions as a stabilizing mechanism rather than merely a compliance tool, the study advances both financial stability theory and the literature on AI governance in finance. The paper contributes a conceptual–methodological framework that integrates innovation, governance, and public interest, offering actionable policy insights for regulators, financial institutions, and FinTech firms. More broadly, the analysis underscores the importance of governing AI-enabled finance in a manner that supports long-term financial stability, equity, and national economic resilience.
FinTech, artificial intelligence, financial stability, governance, algorithmic accountability, consumer protection, United States
Trade Realignments, Exchange Rate Volatility, and Financial Stability in Sub-Saharan Africa
This study examines the impact of trade realignment, exchange rate volatility, and financial depth on financial stability in Sub-Saharan Africa. Recognizing the critical role of financial stability in promoting economic growth and resilience, the study aims to analyze both the long-run and short-run dynamics of macro-financial interactions and provide evidence-based policy insights. The scope of the study covers a panel of 40 Sub-Saharan African countries over the period 2000 to 2024. A Panel Vector Error Correction Model (VECM) is employed to capture the cointegration relationships and short-run adjustments among financial stability, trade realignment, exchange rate volatility, GDP growth, inflation, and financial depth. The findings reveal a significant long-term relationship between financial stability and the explanatory variables, with deviations from equilibrium gradually corrected over time. In the short run, financial stability responds to lagged effects of exchange rate volatility, trade realignment, financial depth, GDP growth, and inflation, highlighting the delayed transmission of shocks. The joint significance of these variables confirms that short-run fluctuations in financial stability are influenced by their combined effects rather than isolated changes. The study recommends enhancing financial sector depth, carefully managing trade realignments, mitigating exchange rate volatility, controlling inflation, and coordinating macroeconomic and financial policies to strengthen resilience. Policy implications include integrating trade and financial sector policies, implementing forward looking risk management frameworks, and promoting regional coordination to reduce cross-border vulnerabilities. Future research should explore the role of digital finance, country-specific heterogeneity, external shocks, and institutional quality in shaping financial stability in Sub-Saharan Africa.
Financial Stability, Trade Realignment, Exchange Rate Volatility, Financial Depth, GDP Growth, Inflation
Mega-Sporting Events and Soft Power: Perceived Changes in Qatar’s Nation Brand Following the 2022 FIFA World Cup
Countries increasingly use mega-sporting events as instruments of soft power and nation branding, yet empirical evidence of their impact on international perceptions remains limited, particularly in non-Western contexts. This study examines changes in Qatar’s nation brand following the 2022 FIFA World Cup by assessing how international audiences evaluated key nation-brand dimensions before and after the event. Drawing on Anholt’s Nation Brand Index framework, a quantitative, perception-based design was employed using a cross-sectional survey of an international sample. Respondents retrospectively assessed Qatar across six dimensions: governance, culture, people, tourism, exports, and immigration and investment. The findings indicate a statistically significant but modest improvement in Qatar’s overall international image. Perceptual gains were most evident in the dimensions of people, culture, and governance, while economically oriented dimensions showed limited or no change. These results suggest that the World Cup functioned as a reputational enhancer rather than a transformative nation-branding intervention, generating selective and uneven effects. The study contributes to nation-branding and sports-diplomacy research by providing perception-based evidence from a non-Western context and highlighting the importance of aligning mega-events with long-term strategic initiatives to achieve sustainable soft-power outcomes.
Nation Branding, Soft Power, Nation Brand Index, Mega Sporting Events, Qatar
The Impact of Shanghai Cooperation Organization Membership on Pakistan’s Manufacturing Exports: A Difference-in-Differences Gravity Analysis
This study examines the impact of Pakistan’s membership in the Shanghai Cooperation Organization (SCO) on its manufacturing export performance, employing a Difference-in-Differences (DID) framework within a gravity-type trade model. Using panel data covering pre- and post-integration periods, the analysis compares Pakistan’s manufacturing exports to SCO member countries with those to non-SCO trading partners to identify the causal effect of SCO accession in 2017. Descriptive evidence indicates a gradual but more stable growth in manufacturing exports following SCO membership, with notable sectoral variation across industries. Empirical results reveal a positive and statistically significant effect of SCO membership on Pakistan’s manufacturing exports, confirming that regional integration has enhanced trade performance beyond pre-existing trends. The validity of the DID approach is supported by pre-treatment parallel trend tests, while robustness checks including placebo tests and Poisson Pseudo-Maximum Likelihood (PPML) estimation confirm the consistency of the findings. Heterogeneity Sectoral analysis shows stronger export growth in healthcare-related products, whereas traditional sectors such as textiles, minerals, and rubber exhibit relatively modest gains, reflecting ongoing structural constraints. Overall, the findings suggest that SCO integration has contributed positively to Pakistan’s manufacturing export expansion, though the benefits remain uneven across sectors. The study underscores the importance of complementary domestic policies, industrial upgrading, and trade facilitation measures to fully realize the export potential offered by regional economic integration.
SCO; Manufacturing Exports; Difference-in-Differences; Regional Integration; Gravity Model; Pakistan
Bridging Prediction and Profit: Deep Learning models with Trading Evaluation for FTSE 100
This study examines stock price forecasting for FTSE 100 companies using deep learning and XAIl. The research addresses the disconnect between predictive accuracy and interpret ability in financial models by integrating data-driven forecasting with transparent feature attribution. Four neural architectures: LSTM2, Bi-LSTM3, GRU4 and CNN5 are compared to classical benchmarks: SMA6 and EMA7. Models are trained on OHLCV8 data augmented with technical indicators. Evaluation uses a threshold-based trading strategy. The findings indicate that a lower prediction error does not necessarily result in higher profitability. Although LSTM achieved the lowest prediction error, GRU and Bi-LSTM produced more stable cumulative returns (16%), compared to the EMA benchmark (2%). SHAP9 analysis demonstrates that recent price movements and momentum indicators, particularly SMA, drive model decisions.
FTSE 100, LSTM, GRU, Explainable AI, SHAP, Trading strategy, Stock market prediction, BILSTM, financial data, machine learning
Trends and Gaps in the Literature about Green Washing in Asia
This bibliometric study explores the trends and gaps in the literature on greenwashing in Asia, analyzing key research patterns, thematic clusters, and geographical contributions. The results highlight an increasing scholarly focus on greenwashing, particularly from 2021 onwards, reflecting growing awareness of environmental issues and deceptive corporate sustainability practices. China and India emerge as leading contributors, with institutions like Shandong University and Massey University playing prominent roles in advancing the research. Thematic clusters emphasize topics such as corporate social responsibility, green marketing, sustainable development, and circular economy, with "greenwashing" serving as a central concept linking these themes. Despite the rising volume of research, gaps persist in understanding the effectiveness of regulatory frameworks, consumer perceptions, and cross-sectoral impacts of greenwashing. This study underscores the need for interdisciplinary approaches and actionable strategies to address greenwashing, supporting authentic sustainability practices and policies.
Reconstruction as a Lever for Sustainable Development in Gaza: Between Reality and Prospects for Recovery After the 7 October 2023 War
The 2023 conflict in the Gaza Strip resulted in unparalleled human, material, and structural damage, valued at approximately USD 18.5 billion, equivalent to almost a full year of Palestinian GDP. Beyond the extent of physical devastation, the hostilities revealed the profound vulnerability of Gaza’s economy, public systems, and communal structure, and severely aggravated hardship, joblessness, and involuntary movement. This analysis explores how post-2023 rebuilding can surpass a brief humanitarian reaction to become a strategic instrument for enduring progress in Gaza. Employing a descriptive–analytical and specific-example method, the research utilizes recent evaluations by the World Bank, the UN, and ESCWA (2024–2025) to chart the extent and sectoral nature of the harm, pinpoint major organizational, fiscal, and political barriers, and dissect the connections between rebuilding and the economic, societal, and ecological foundations of permanence. The paper suggests a conceptual model that regards reconstruction as an evolving procedure rooted in “build back superior” tenets, strong local administration, and alignment with the 2030 Agenda. It then details a policy plan for 2025–2035 connecting accommodation, essential structures, earning potential, welfare provisions, rubble handling, and clean power into a unified national blueprint. The paper finishes by asserting that progress in Gaza will only serve as a true impetus for lasting development if it revitalizes populations, the marketplace, and organizations concurrently, under a domestically controlled structure that harmonizes emergency assistance with protracted structural changes.
Reconstruction; Gaza Strip; 2023 conflict; Enduring progress; Post-conflict recuperation; Relief effort; Durability; Leadership; Rubble handling; Clean economy; Societal fairness.
How Smart City Initiatives Economically Affect Small and Medium-Sized Enterprises (SMEs): Innovation Capacity and Competitiveness – A Comparative Study of Japan and the UK
This paper will look at the impact of smart city projects on the competitiveness and economic performance of small to medium enterprise (SMEs) in the United Kingdom and Japan. The selection of a qualitative comparative research design to be used was based on the systematic analysis of the academic literature, national policy papers and institutional reports. The results indicate that the Japanese long-term, highly centralized type of governance helps in facilitating systemic coordination, structural integration, whereas the UK model that is decentralized, market-driven encourages flexibility, fast experimentation, and data-driven innovation. Nevertheless, having differences, both models unveil that smart city programs can only be translated into the competitiveness of SMEs in case of coherent policies, digital literacy, and collaborative ecosystems. The research has implications on policymakers who want to reconcile technology change with an inclusive economic development in the urban settings.
Smart cities, Small and medium-sized enterprises (SMEs), Innovation and competitiveness, Society 5.0 (Japan), Digital transformation, Open data and innovative governance, Comparative economic analysis (Japan–UK)
Tourism as a Catalyst for Economic Diversification in Saudi Arabia: Vision 2030 and Beyond
Saudi Arabia’s Vision 2030 reform program positions tourism as a cornerstone for economic diversification, aiming to reduce reliance on oil revenues and foster a knowledge-based economy. This paper critically examines the evolution of Saudi Arabia’s tourism sector under Vision 2030, analyzing its economic, social, and cultural impacts while identifying key challenges and opportunities. Drawing on secondary data from the Saudi Ministry of Tourism, OECD, UNWTO, and World Bank, the study reviews flagship projects such as NEOM, the Red Sea Project, and Diriyah Gate. It also explores the role of technology, sustainability, and global partnerships in shaping the sector’s future. The paper concludes with a comprehensive policy framework and actionable recommendations to ensure sustainable, inclusive, and competitive tourism growth by 2030.
Tourism, Economic Diversification, Vision 2030, Saudi Arabia, NEOM, Red Sea Project, Sustainability, Cultural Heritage, Employment, Smart Tourism
Does Corporate Governance Improve Bank Performance? A study of Rural Banks in West Java, Indonesia
The objective of this research is to examine the correlation between Sound Corporate Governance and Financial Performance within Rural Banksing Institutions. The design/methodology uses balanced panel data multiple regression analysis on EViews 13 software. The study sample comprises Rural Banks located in West Java Province. The study's findings demonstrate that the inclusion of female directors significantly enhances ROA. The directors adversely impact both ROA and ROE. Furthermore, the board of commissioners and independent commissioners have no impact on ROA but negatively impact ROE. This study is unique in that it focuses on Rural Banks, which have received less attention in earlier research.
Keywords: Good Corporate Governance, Financial Performance, Rural Banks.
Identifying Hidden Fraud Indicators through Longitudinal Analysis of U.S. Financial Misconduct
Systemic risk of fraud in U.S. finance and health care has continued to have an adverse impact on the economy, stretch government programs and reduce public trust in institutions. This study provides a 10 year cross-sectoral (finance and health care) study (years 2010-2020) utilizing databases from the Consumer Financial Protection Bureau (CFPB), Securities Exchange Commission (SEC), Department of Justice (DOJ), Center for Medicare Services (CMS) and Health and Human Services Office of Inspector General (HHS-OIG) to identify potential early warning signs which may occur prior to the detection of fraud by regulatory bodies. Using Longitudinal Trend Analysis, Correlation Mapping, K-means Clustering, and Regression Modeling; this study demonstrates a sustained increase in Fraud Occurrence Rates (FOR) for each sector, and a high positive correlation between the two sectors (Sectoral Correlation Index SCI= .72). The three early warning signs identified within this study: Complaint Escalation Rate (CER), Detection Lag (DL), and Fraud Method Diversity (FMD); were found to possess statistically significant predictive capability for future confirmed fraud events. The trends also demonstrated that spikes in public complaints and healthcare recoveries could potentially serve as an actionable signal to regulators of the need for increased oversight or investigation. Based upon these findings, the study proposes an Early Warning Fraud Detection Framework (EW-FDF) and advocates for the development of a Unified Fraud Intelligence Network (UFIN) to enable predictive analytic capabilities across agency boundaries.
Hidden Fraud Indicators, Fraud Analysis Longitudinal, Financial Misconduct, Predictive Fraud Detection and U.S. Regulatory Oversight
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ESG and Commercial Credit Financing: Based on Government Subsidies and Polluted Areas
This paper investigates whether and how firms’ Environmental, Social and Governance (ESG) performance affects commercial credit financing (SCF) in the context of Chinese listed companies. Using panel data on A-share firms and ESG ratings, this study constructs a comprehensive measure of SCF based on supplier-provided trade credit and estimate fixed-effects models. The results show that ESG performance is positively and significantly associated with SCF. Further analysis reveals pronounced heterogeneity: ESG enhances SCF only in non-heavily polluting industries and among firms receiving government subsidies, while the effect is insignificant for heavily polluting and non-subsidised firms. These findings suggest that ESG operates as a credible financing signal when it reflects voluntary engagement. The results imply that firms can treat ESG as part of their financing strategy and use it to improve access to supplier credit. Suppliers can also incorporate ESG indicators into credit-risk assessment. Policymakers may strengthen ESG disclosure rules and subsidy design to support more efficient, sustainability-oriented supply-chain finance.
Copyright (c) 2025 Journal of Economics, Finance and Accounting Studies https://creativecommons.org/licenses/by-nc/4.0
Accountability of Mosque Financial Management: A Case Study of Pangeran Diponegoro Mosque, Yogyakarta, Indonesia
This study aims to describe how the Pangeran Diponegoro Mosque instituted open and accountable financial management related to the mosque’s activities. Specifically, this study aims to: (1) describe the forms of activity and financial reporting practiced at the mosque; and (2) analyze the extent to which these practises reflect principles of accountability and transparency for non-profit entities; and (3) identify supporting and constraining factors that shape accountability in reporting within a mosque funded by its community. The research employed a qualitative approach with a single-case study design, utilizing in-depth interviews with mosque administrators (takmir) and congregants/donors, in addition to an analysis of financial documents. Data analysis was conducted by adapting accountability and transparency indicators from previous studies. The results indicate that financial reporting is conducted regularly through simple cash reports, which include receipts, expenditures, and balances, along with occasional announcements to the congregation. This practice incorporates elements of accountability and transparency, as evidenced by periodic reports, deliberations, and informal disclosures. However, it lacks support from specific internal regulations, has never undergone third-party auditing, and does not leverage digital media platforms, such as Instagram, for disseminating financial information. Key supporting factors include the management's religious commitment, social closeness with the congregation, and the convenience of cashless donations via transfer/QR. Conversely, inhibiting factors involve the limited accounting skills of mosque committees and the absence of up-to-date operational guidelines from the Ministry of Religious Affairs regarding a practical reporting format for mosque financial management. This research provides input to the Ministry of Religious Affairs regarding the need to develop national guidelines for mosque financial reporting that are simpler, more uniform, and in accordance with Sharia principles. It also highlights the importance of improving basic financial reporting capacity at the mosque committee level to strengthen accountability and transparency across mosques in Indonesia.
Current Snapshot of Business Climate and Sustainable Economy. Does AI Play a Crucial Role?
This study aims to provide an analysis of the business climate on a sustainable economy by considering the effects of AI adoption in the era of global uncertainty (2024). The study obtained data from 50 countries, which were selected based on the specified criteria. The main analysis of this study applied moderation regression analysis, while the additional analysis applied multiple regression analysis. This research uses the EViews 13 application to analyze the data. The main analysis indicates that the business climate has a positive effect on the sustainable economy, and AI does not moderate the influence of the business climate on the sustainable economy. Additional analysis shows consistency that all indicators in the business climate have a positive effect on the sustainable economy. The findings of this study are contributing to the motivation and evaluation of each country to enhance a sustainable economy through the implementation of a stable and supportive business climate, as well as the promotion of the adoption of AI as a catalyst for sustainability in various sectors, specifically business and the economy.
Sustainable Economy, Business Climate, Artificial Intelligence
Copyright (c) 2025 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by-nc/4.0
The Economics of Fast Food: Impact on Society, Employment, and Consumption Patterns: A Comprehensive Study Integrating Global and Indian Perspectives
This research article examines the economics of the fast-food industry, focusing on its societal impact, employment dynamics, and evolving consumption patterns. Integrating global and Indian perspectives, the study traces the historical development of fast food, analyses employment trends including youth and gig economy participation, and investigates consumer behavior and pricing strategies. It further scrutinizes public health and environmental challenges, evaluates policy responses, and considers the industry's future in light of technological and regulatory trends. The study employs a data-driven approach, incorporating infographics, and tables to illustrate key trends. The findings offer valuable insights for students, researchers, and policymakers into the complexities and implications of the fast-food sector worldwide and in India.
Fast food industry, economics, consumption patterns, public health, consumer behavior, pricing strategies, gig economy, environmental challenges, sustainability initiatives, market adaptation.
Copyright (c) 2025 https://creativecommons.org/licenses/by-nc/4.0
Internal, External, and Forensic Audits as Determinants of Fraud Prevention in Ghana’s Metropolitan, Municipal, and District Assemblies
Fraud and corruption are major global challenges that have a profound effect on individuals, countries, and economies. They incur substantial costs and hinder societal advancement. In Ghana, fraud and corruption present serious obstacles for the Metropolitan, Municipal, and District Assemblies (MMDAs), compromising governance and development efforts. This research assesses how well internal, external, and forensic audits function in curbing fraudulent financial activities within Ghana's MMDAs. A quantitative cross-sectional survey design was utilised to gather data from 343 finance directors, accountants, and internal auditors across 261 MMDAs. Descriptive statistics and multiple regression analysis were used to evaluate the data. The results indicate that internal audits are viewed as effective for identifying early indicators of financial mismanagement (mean = 4.87) and for preventing fraud (mean = 4.60), although issues like lack of independence (mean = 4.23) and insufficient resources (mean = 4.23) exist. External audits are conducted regularly (mean = 5.44) but face challenges with the weak enforcement of their recommendations (mean = 2.56). Forensic audits, despite their limited usage (mean = 1.68 regarding the hiring of forensic auditors), show the most substantial association with fraud reduction (β = 0.228, p < 0.001). Together, audit activities account for 12.2% (R² = 0.122) of the variance in fraudulent practices, revealing their significant yet constrained effectiveness. The study concludes that although audits are vital for preventing fraud, their success is undermined by systemic shortcomings. It suggests enhancing forensic audit capabilities, ensuring the enforcement of external audit recommendations, and preserving the independence of internal audits to improve fraud prevention in Ghana's local governments. These findings add value to both academic discussions and practical policy reforms in the management of public sector finances.
Predictive Modeling of U.S. Stock Market Trends Using Hybrid Deep Learning and Economic Indicators to Strengthen National Financial Resilience
The stock markets are too complex to predict, despite their non-linearity, volatility, and multifactorial nature. Current econometric and deep learning models often do not take into account the macroeconomic context, which restricts predictive accuracy at times of financial volatility. This paper attempts to fill this gap by proposing a Hybrid Deep Learning Model (HDLM), a machine that combines Long Short-Term Memory (LSTM) networks, Convolutional Neural Networks (CNNs), and an attention mechanism that can jointly include both the dynamic behavior of the market and the economic links in the trends of the U.S. market. The analysis is based on a strictly tested data set of 20102023, which was retrieved from the Federal Reserve Economic Data (FRED), Yahoo Finance, and the Bureau of Economic Analysis. These include all significant equity indexes such as the S&P 500, NASDAQ Composite, and Dow Jones Industrial Average, prime macroeconomic indicators such as the GDP growth, inflation rates, interest rates, and unemployment levels. Correlation analysis, multiple regression, Granger causality tests, Johansen cointegration procedures, and volatility modeling using ARCH GARCH models are the statistical methods used. The findings show that GDP growth has a statistically significant positive impact on market returns ( = 0.58, p = 0.001), though inflation ( = -0.21, p = 0.021), interest rates ( = -0.34, p = 0.002), and unemployment ( = -0.42, p = 0.001) have significant negative predictive power (R 2 = 0.68). The HDLM achieves a better predictive performance with RMSE = 1.89, MAPE = 4.9% and DA = 83.6% > 23% better than the baseline LSTM and CNN- LSTM settings. The model minimizes prediction error by 36.1% in cases of strong market shocks, which occur under simulated stress conditions, confirming an increased financial strength. Taken together, the results of the studies support the idea that the incorporation of macroeconomic intelligence into hybrid neural systems significantly enhances forecast reliability and stability of the system. The current research, therefore, adds a strong, interpretive, and policy-related framework to the research fields of predictive finance and resilience engineering systems to predict market upheavals.
Deep learning, financial resilience, macroeconomic indicators, stock market forecasting, U.S. economy
Copyright (c) 2023 https://creativecommons.org/licenses/by-nc/4.0
The Internal Audit Roles and Financial Discipline of the Technical Universities in Ghana: The Moderating Role of Effective Leadership
This study examined the relationship between internal audit roles and financial discipline, with effective leadership as a moderating factor, in Ghana’s technical universities. Using an explanatory research design, data were collected from 127 key officeholders across the Technical Universities in Ghana. The findings show that internal auditors play a pivotal role in promoting financial discipline through strategic advisory services, operational efficiency, risk management, reporting, and compliance enforcement. More importantly, the results demonstrate that effective leadership practices centred on integrity, transparency, accountability, strategic vision, and sound financial decisions significantly strengthen the positive effects of internal auditing on financial discipline. The financial discipline remains a significant challenge, as evidenced by ongoing issues such as fund misallocation, procurement manipulation, weak budgeting practices, ineffective debt management, and the neglect of internal audit recommendations. The study recommends that technical universities complement national policies and legal frameworks with internally developed financial discipline policies tailored to their specific contexts. Central to this should be the establishment of a comprehensive Code of Ethics for Financial Discipline, with explicit provisions requiring strict adherence by leaders and senior officials and enforcing stringent penalties for breaches. Such context-specific policies, aligned with national directives, will reinforce ethical conduct, strengthen financial integrity, and promote sustainable accountability in technical universities.
Financial Discipline, Strategic Advisory Services, Risk Management, Policies, Financial Mismanagement, Sound Financial Decisions, budgeting
Corporate Investment in Indonesia: Literature Review
This research aims to conduct in-depth research on corporate investment decisions in Indonesia. The company's investment decision is an important factor in a business that will determine the company's condition in the future. There are cases of failure due to investment decisions made by companies, which is not in line with several studies that show the importance of corporate investment decisions. The investment decisions made by a company are different from those made by individual investors, this study will only analyze the investment decisions made by a company. Analysis is needed to find out the factors that affect a company's investment decisions or are influenced by investment decisions. The method used in this study is systematic literature review analysis. Using a database from a journal indexed by Sinta with the addition of the Scopus base with cases in Indonesia. Data collection uses systematic stages, which are then reviewed manually by the document. A company's investment decision is influenced by several variables and divided into financial and non-financial categories. This research has implications for investment decisions made by a company and is an additional literature for researchers in the future.
Investment Decisions, Corporate Investment Decisions, Literature Review, Corporate Investment
Cryptocurrency Accounting Frameworks and Tax Compliance Challenges: Toward a Unified Digital Financial Reporting Model
The swift growth of cryptocurrencies has highlighted several issues with current accounting and tax compliance regimes given that there is little to no accounting guidance in the current standards like IFRS or US GAAP regarding the measurement, recognition and disclosure of crypto-assets . (Rajaram & Aboluwodi, 2024; Mpanza, ). This paper analyzes the existing literature along with case practices to present the main concerns behind unreliable financial reporting and inefficient tax enforcement as being those of volatility, lack of clarity in legal categorization, problems of tax jurisdiction in cross- border transactions, and anonymity. Drawing on and comparing existing structures found in policy documents, financial statements, and guidelines, it makes the case for a theory of a singular digital financial reporting method utilizing the blockchain as a financial reporting and auditing platform through standardized measures and advanced disclosures of tax information. The suggested model attempts to harmonize accounting measurement with sound tax compliance as well as safeguard the users of financial statements from artificial liquidity, profitability and asset measurement or valuation. The results apply particularly to standard setters, regulators, and crypto-assets firms to move towards coherence in regulation and risk reduction in the digital financial ecosystem.
field of accounting for cryptocurrencies. Financial reporting of digital assets. Blockchain accounting standards. Crypto taxation issues. Consolidated model of reporting. IFRS and cryptocurrency value